<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">J. Pina</style></author><author><style face="normal" font="default" size="100%">PU Lima</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A glass furnace operation system using fuzzy modelling and genetic algorithms for performance optimisation</style></title><secondary-title><style face="normal" font="default" size="100%">Engineering Applications of Artificial Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://oa.uninova.pt/1882/</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">7-8</style></number><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">681–690</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;An architecture for the operation of a recuperative-type glass furnace is introduced in this paper. It is based on a hierarchical scheme, with two main parts: process optimisation and process modelling. Process optimisation is carried out by an expert controller, and uses genetic algorithms to solve a multiobjective optimisation problem. Process modelling is performed by a learning system, based on a fuzzy learning-by-examples algorithm. Results of real and simulated experiments with the glass manufacturing process are presented.&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">n/a</style></notes></record></records></xml>